Abstract
In this paper, a novel multi-agent control system incorporating hybrid intelligence and its physical testbed are presented. The physical testbed is equipped with a large number of embedded devices interconnected by three types of physical networks. It mimics a ubiquitous intelligent environment and allows real-time data collection and online system evaluation. Human control behaviours for different physical devices are analysed and classified into three categories. Physical devices are grouped based on their relevance and each group is assigned to a particular behaviour category. Each device group is independently modelled by either fuzzy inference or neural network agents according to the behaviour category. Comparative analysis shows that the proposed multi-agent control system with hybrid intelligence achieves significant improvement in control accuracy compared to other offline control systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Augusto, J.C., Nugent, C.D. (eds.): Designing Smart Homes. LNCS (LNAI), vol. 4008. Springer, Heidelberg (2006)
Ducatel, K., Bogdanowicz, M., Scapolo, F., Burgelman, J.-C.: Scenarios for Ambient Intelligence in 2010. Information Soc. Technol. Advisory Group (ISTAG), Inst. Prospective Technol. Studies (IPTS), Seville (2001)
Brooks, R.A.: The Intelligent Room Project. In: Second International Conference on Cognitive Technology, pp. 271–278 (1997)
Philips, B.A.: Metaglue: A Programming Language for Multi-Agent Systems. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (1999)
Gajos, K.: A Knowledge-Based Resource Management System For The Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2000)
Kulkarni, A.A.: A Reactive Behavioral System for the Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2002)
Rutishauser, U., Schaefer, A.: Adaptive Building Intelligence: A multi-Agent approach. Diploma thesis, University of Applied Science Rapperswil, Switzerland and Institute of Neuroinformatics, Swiss Federal Institute of Technology and University of Zurich, Switzerland (2002)
Doctor, F., Hagras, H., Callaghan, V.: A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environment. IEEE Trans. Sys. Man Cybern. 35(1), 55–65 (2005)
Mozer, M.: The neural network house: An environment that adapts to its inhabitants. In: Proc. Amer. Assoc. Artif. Intell. Spring Symp. Intell. Environ. pp. 110–114 (1998)
Brumitt, B., Cadiz, J.J.: Let There Be Light! Comparing Interfaces for Homes of the Future. Microsoft Research, Redmond, WA 98052, MSR-TR-2000-92 (2000)
Yoshihama, S., Chou, P., Wong, D.: Managing Behavior of Intelligent Environments. In: Proc. of the First IEEE Int. Conf. on Pervasive Comp, pp. 330–337. IEEE Computer Society Press, Los Alamitos (2003)
Tsai, C.F., Wu, H.C.: MASSIHN: A Multi-Agent Architecture for Intelligent Home Network Service. IEEE Trans. on Consumer Electronics 46, 505–514 (2002)
Wang, K.I., Abdulla, W.H., Salcic, Z.: Distributed Embedded Intelligence Room with Multi-agent Cooperative Learning. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.J.-P. (eds.) UIC 2006. LNCS, vol. 4159, pp. 147–156. Springer, Heidelberg (2006)
Echelon Corporation, LonWorks Overview (February 2006), http://www.echelon.com/solutions/overview/default.htm
Wang, K.I., Abdulla, W.H., Salcic, Z.: Multi-agent fuzzy inference control system for intelligent environments using JADE. In: Proc. of 2nd IET Int. Conf. on Intel. Environ. pp. 285–294 (2006)
Wang, K.I., Abdulla, W.H., Salcic, Z.: A Multi-Agent System for Intelligent Environments using JADE. In: IEE Int. Workshop on Intell. Environ. pp. 86–91 (2005)
Kecman, V.: Learning and Soft Computing: Support Vector Machines. In: Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge (2001)
Castellano, G., Fanelli, A.M., Mencar, C.: Generation of interpretable fuzzy granules by a double clustering technique. Arch. Contr. Sci. 12(4), 397–410 (2002)
Wang, L.X.: The MW method completed: A flexible system approach to data minig. IEEE Trans. Fuzzy Syst. 11(6), 678–782 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wang, K.IK., Abdulla, W.H., Salcic, Z. (2007). Multi-agent System with Hybrid Intelligence Using Neural Network and Fuzzy Inference Techniques. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)